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📋📋📋本文目录如下：🎁🎁🎁
目录
💥1 概述
PID 算法是指“比例”“积分”“微分”三个元素。P指当无人机收到外界的干扰后，机身对产生姿态偏移回到稳定的时间，即用来控制从不稳定到稳定的响应时间，P 越大响应越快；I 指当无人机从不稳定回到稳定状态一段时间内积累的误差，只修正 P 是无法达到的期望姿态值，也叫静态差值；D 是为了加强对机体变化的快速响应，对 P 有抑制作用，二者具有互斥性。
📚2 运行结果
起点和终点可以任意设置。
部分代码：
function [t_out, s_out] = simulation_3d(trajhandle, controlhandle)
% NOTE: This srcipt will not run as expected unless you fill in proper
% code in trajhandle and controlhandle
% You should not modify any part of this script except for the
% visualization part
%
% ***************** QUADROTOR SIMULATION *****************
% *********** YOU SHOULDN'T NEED TO CHANGE ANYTHING BELOW **********
addpath('utils');
% real-time
real_time = true;
% max time
max_time = 20;
% parameters for simulation
params = sys_params;
%% **************************** FIGURES *****************************
disp('Initializing figures...');
h_fig = figure;
h_3d = gca;
axis equal
grid on
%view(3);
view(48.8, 25.8);
xlabel('x [m]'); ylabel('y [m]'); zlabel('z [m]')
quadcolors = lines(1);
set(gcf,'Renderer','OpenGL')
%% *********************** INITIAL CONDITIONS ***********************
disp('Setting initial conditions...');
tstep = 0.01; % this determines the time step at which the solution is given
cstep = 0.05; % image capture time interval
max_iter = max_time/cstep; % max iteration
nstep = cstep/tstep;
time = 0; % current time
err = []; % runtime errors
% Get start and stop position
des_start = trajhandle(0, []);
des_stop = trajhandle(inf, []);
stop_pos = des_stop.pos;
x0 = init_state(des_start.pos, 0);
xtraj = zeros(max_iter*nstep, length(x0));
ttraj = zeros(max_iter*nstep, 1);
x = x0; % state
pos_tol = 0.01;
vel_tol = 0.01;
%% ************************* RUN SIMULATION *************************
disp('Simulation Running....');
% Main loop
for iter = 1:max_iter
timeint = time:tstep:time+cstep;
tic;
% Initialize quad plot
if iter == 1
QP = QuadPlot(1, x0, 0.1, 0.04, quadcolors(1,:), max_iter, h_3d);
current_state = stateToQd(x);
desired_state = trajhandle(time, current_state);
QP.UpdateQuadPlot(x, [desired_state.pos; desired_state.vel], time);
h_title = title(sprintf('iteration: %d, time: %4.2f', iter, time));
end
% Run simulation
[tsave, xsave] = ode45(@(t,s) quadEOM(t, s, controlhandle, trajhandle, params), timeint, x);
x = xsave(end, :)';
% Save to traj
xtraj((iter-1)*nstep+1:iter*nstep,:) = xsave(1:end-1,:);
ttraj((iter-1)*nstep+1:iter*nstep) = tsave(1:end-1);
% Update quad plot
current_state = stateToQd(x);
desired_state = trajhandle(time + cstep, current_state);
QP.UpdateQuadPlot(x, [desired_state.pos; desired_state.vel], time + cstep);
set(h_title, 'String', sprintf('iteration: %d, time: %4.2f', iter, time + cstep))
time = time + cstep; % Update simulation time
t = toc;
% Check to make sure ode45 is not timing out
if(t> cstep*50)
err = 'Ode45 Unstable';
break;
end
% Pause to make real-time
if real_time && (t < cstep)
pause(cstep - t);
end
% Check termination criteria
if terminate_check(x, time, stop_pos, pos_tol, vel_tol, max_time)
break
end
end
%% ************************* POST PROCESSING *************************
% Truncate xtraj and ttraj
xtraj = xtraj(1:iter*nstep,:);
ttraj = ttraj(1:iter*nstep);
% Truncate saved variables
QP.TruncateHist();
% Plot position
h_pos = figure('Name', ['Quad position']);
plot_state(h_pos, QP.state_hist(1:3,:), QP.time_hist, 'pos', 'vic');
plot_state(h_pos, QP.state_des_hist(1:3,:), QP.time_hist, 'pos', 'des');
% Plot velocity
h_vel = figure('Name', ['Quad velocity']);
plot_state(h_vel, QP.state_hist(4:6,:), QP.time_hist, 'vel', 'vic');
plot_state(h_vel, QP.state_des_hist(4:6,:), QP.time_hist, 'vel', 'des');
if(~isempty(err))
error(err);
end
disp('finished.')
t_out = ttraj;
s_out = xtraj;
end
🎉3 参考文献
部分理论来源于网络，如有侵权请联系删除。
[1]Phill Weston,JOUAV.
[2]刘新宇,桑晨,李媛媛,游璧铭,刘畅.基于PID算法的巡逻无人机[J].现代信息科技,2022,6(20):141-145+151.DOI:10.19850/j.cnki.2096-4706.2022.20.033.